- Intro
- Preface
- Organization
- Contents
- Developing Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis
- 1 Introduction
- 2 Method
- 2.1 Subjects and Image Preprocessing
- 2.2 Proposed Weighted Correlation Kernel
- 2.3 Architecture of the Proposed Wc-Kernel Based CNN
- 3 Experiments
- 4 Conclusion
- References
- Robust Contextual Bandit via the Capped-2 Norm for Mobile Health Intervention
- 1 Introduction
- 2 Preliminaries
- 3 Robust Contextual Bandit with Capped-2 Norm
- 3.1 Algorithm for the Critic Updating
- 3.2 Algorithm for the Actor Updating
- 4 Experiments
- 4.1 Datasets
- 4.2 Experiments Settings
- 4.3 Results and Discussion
- 5 Conclusions and Future Directions
- References
- Dynamic Multi-scale CNN Forest Learning for Automatic Cervical Cancer Segmentation
- Abstract
- 1 Introduction
- 2 Proposed Cluster-Based Dynamic Multi-scale Dynamic Forest
- 2.1 Root Node CNN Architecture
- 2.2 Cascaded CNNs
- 2.3 Proposed CNN-Based Dynamic Multi-scale Tree (DMT)
- 2.4 Proposed CK+1DMF Learning Framework
- 3 Results and Discussion
- 4 Conclusion
- 3.2 Training Parameters
- 3.3 Evaluation
- 4 Results
- 4.1 FROC Analysis
- 4.2 Reconstructed Images
- 5 Conclusion and Discussion
- References
- CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
- 1 Introduction
- 2 Methods
- 2.1 CT Image Enhancement
- 2.2 Lesion Segmentation
- 3 Experimental Results and Analyses
- 4 Conclusions
- References
- Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality Similarity
- Abstract
- 1 Introduction
- 2 Method
- 2.1 Loss Function Based on Intra-modality Similarity
- 2.2 Inter-modality Registration Network
- 2.3 Spatial Transformation Layer
- 3 Experimental Results
- 3.1 Registration Results
- 4 Conclusion
- References
- Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis
- 1 Introduction
- 2 Materials and Preprocessing
- 3 Proposed Method
- 3.1 Regional Abnormality Representation
- 3.2 Brain-Wise Feature Extraction and Classifier Learning
- 4 Experimental Settings and Results
- 4.1 Experimental Settings
- 4.2 Results and Discussion
- 5 Conclusion
This book constitutes the proceedings of the 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018.The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.
(source: Nielsen Book Data)
